Radiation biomarker screening and dose reconstruction based on machine learning

PURPOSE: Nuclear emergency medical rescue is a critical component of the nuclear emergency response system, playing a vital role in safeguarding public life and health. To address the urgent need for rapid, wide-range radiation biodosimetry in nuclear emergency scenarios, this study utilized female C57BL/6J mice model to develop a machine learning (ML) framework for radiation-responsive biomarker screening and dose reconstruction across a broad dose range (0-12 Gy), laying a foundational preclinical basis for future translational research in human biodosimetry. MATERIALS AND METHODS: The blood sample of mice was collected at 24 hours and seven days post-irradiation. The gene expression was evaluated by transcriptomic sequencing. Further, differential expression analysis, Spearman's correlation filtering and Boruta algorithm were sequentially employed for screening radiation biomarkers. The stacking model integrating multiple ML algorithm was established for dose reconstruction. The gene expression was ultimately validated by more practical qRT-PCR method. RESULTS: of 0.952 and an RMSE of 0.938 Gy, significantly outperforming conventional regression analysis and individual ML models. Further refinement reduced the gene panel to just 15 key markers while preserving reconstruction accuracy comparable to the full 172-gene model. Experimental validation via qRT-PCR confirmed the reliability of these biomarkers, demonstrating the framework's potential for translation into a field-deployable diagnostic platform for radiation exposure assessment. CONCLUSIONS: We established ML framework that incorporates a multi-stage biomarker screening strategy and a stacking ML mode, to achieve rapid and accurate dose reconstruction across a wide dose range. This methodology provides a novel technical solution for nuclear emergency medical response.